Method and system  for screening and monitoring of cardiac diseases

ABSTRACT

Embodiments herein provide a system and method for screening and monitoring of cardiac diseases by analyzing acquired physiological signals. Unlike state of art approaches that consider only synchronized ECG and PPG signals for cardiac health analysis and do not consider PCG which is a critical signal for CAD analysis, the system synchronously captures physiological signals such as photo plethysmograph (PPG), phonocardiogram (PCG) and electrocardiogram (ECG) from subject(s) and builds an analytical model in the cloud for analyzing heart conditions from the captured physiological signals. The system and method provides a fusion based approach of combining the captured physiological signals such as PPG, PCG and ECG along with other details such as subject clinical information, demography information and so on. The analytical model is pretrained using ECG. PPG and PCG along with metadata associated with the subject such as demography and clinical information.

PRIORITY CLAIM

The present application claims priority from Indian provisional patentapplication no. IN-201921026501, filed on Jul. 2, 2019. The entirecontents of the aforementioned application are incorporated herein byreference.

TECHNICAL FIELD

The disclosure herein generally relates to field of health monitoring,and more particularly, to system and method for screening and monitoringof cardiac diseases.

BACKGROUND

In the past century, there has been a shift in the disease pattern anddeath pattern from communicable to non-communicable diseases withcardiovascular diseases being the number one cause. Unfortunately earlystages of the disease are totally asymptomatic and are very difficult todetect by noninvasive methods and as a result most patient come to knowabout the presence of the disease once they have a primary event e.g.myocardial infarction (heart attack) or other complications. A largenumber of these patients die out of hospital and those who survive willhave lifetime risk of secondary events and will have to live withcomplications of primary event throughout the life. Hence there is acompelling need to bring in a system and method which can noninvasivelypredict and aid in early diagnosis of heart diseases and put thesepatients on treatment path and prevent life threatening disease and itscomplications.

Works in literature have been researching on photo plethysmograph (PPG),phonocardiogram (PCG) and electrocardiogram (ECG) acquired from a personand analyzing them for cardiac disease predictions. However, thesesignals are captured asynchronously in sequential manner one afteranother. Multiple features are extracted from these physiologicalsignals. Existing method analyze features of each sensed signal (PPG,PCG, ECG) of a subject under observation independently and then classifyas the subject as having Coronary Artery Disease (CAD) or normal.Machine learning have been used for above classification and the outputof classification is fused for the detection of CAD based on predefinedcriteria. However, there exists a relation among these signals, which ishardly explored for disease analysis. Thus, with existing methodsfocused on independent analysis of the ECG, PPG and PCG affects theaccuracy of disease diagnosis. Few works in literature capture few ofthe physiological signals in synchronous manner for analysis howeverthey limit to only ECG and PPG synchronization without any attempt toconsider synchronously capturing other critical physiological signalswhich contribute towards improving accuracy of cardiac diseasepredictions.

SUMMARY

Embodiments of the present disclosure present technological improvementsas solutions to one or more of the above-mentioned technical problemsrecognized by the inventors in conventional systems. For example, in oneembodiment, a method for screening and monitoring cardiac diseases byanalyzing acquired physiological signals, the method comprising:displaying a User Interface (UI), by one or more hardware processors ofa heart sense device, for enabling entering of metadata comprisingdemography and clinical information associated with a subject among aplurality of subjects screened and monitored via an authenticated accessto the heart sense device, wherein a plurality of probes of the heartsense device are non-invasively attached to the subject.

Further, synchronously acquiring by the one or more hardware processorsvia the plurality of probes, a plurality of physiological signalscomprising an ECG, a PPG, and a PCG of the subject, whereinsynchronously acquiring the plurality of physiological signalscomprises: acquiring each of the plurality of physiological signals as aplurality of segments of data; converting the plurality of segments ofdata corresponding to the physiological signals into a plurality ofdigital segments using an Analog to Digital Converter (ADC); associatingeach of the plurality of digital segments with time stamps;pre-processing each of the plurality of segments with the time stamps todiscard noisy segments and identify a plurality of clean segments;identifying a set of synchronous segments from the plurality of cleansegments based on mapping time stamps, wherein each of the set ofsynchronous segments corresponds to each of the plurality ofphysiological signals, and wherein the set of synchronous segments arecaptured over a configurable predetermined time interval; and displayingthe set of synchronous segments on the UI.

Furthermore, transmitting by the one or more hardware processors, theset of synchronous segments and the metadata of the subject to a cloudserver via an application on a mobile device, wherein the application onthe mobile device communicates with the heart sense device over a shortrange communication interface and enables editing of metadata andpreliminary analysis on the set of synchronous segments via anauthenticated access mechanism.

Furthermore, analyzing using an analytical model in the cloud server,the set of synchronous segments and the metadata of each of theplurality of subjects and predicting a cardiac disease among a pluralityof cardiac diseases, wherein the analytical model is a pretrainedMachine Learning (ML) model.

In another aspect, a system for screening and monitoring cardiacdiseases by analyzing acquired physiological signals, the systemcomprising: a heart sensing device, a mobile device, and a cloud server,wherein: the heart sensing device comprises a memory storinginstructions; one or more Input/Output (I/O) interfaces; and one or morehardware processors coupled to the memory via the one or more I/Ointerfaces, wherein the one or more hardware processors are configuredby the instructions to: display a User Interface (UI) for enablingentering of metadata comprising demography and clinical informationassociated with a subject among a plurality of subjects screened andmonitored via an authenticated access to the heart sense device, whereina plurality of probes of the heart sense device are non-invasivelyattached to the subject;

Further, synchronously acquire via the plurality of probes, a pluralityof physiological signals comprising an ECG, a PPG, and a PCG of thesubject, wherein synchronously acquiring the plurality of physiologicalsignals comprises: acquiring each of the plurality of physiologicalsignals as a plurality of segments of data; converting the plurality ofsegments of data corresponding to the physiological signals into aplurality of digital segments using an Analog to Digital Converter(ADC); associating each of the plurality of digital segments with timestamps; pre-processing each of the plurality of segments with timestamps to discard noisy segments and identify a plurality of cleansegments; identifying a set of synchronous segments from the pluralityof clean segments based on mapping time stamps, wherein each of the setof synchronous segments corresponds to each of the plurality ofphysiological signals, and wherein the set of synchronous segments arecaptured over a configurable predetermined time interval; and displayingthe set of synchronous segments on the UI.

Furthermore, transmit the set of synchronous segments and the metadataof the subject to a cloud server via an application on a mobile device,wherein the application on the mobile device communicates with the heartsense device over a short range communication interface and enablesediting of metadata and preliminary analysis on the set of synchronoussegments via an authenticated access mechanism.

Furthermore, the cloud server is configured to analyze, using ananalytical model, the set of synchronous segments and the metadata ofeach of the plurality of subjects and predicting a cardiac disease amonga plurality of cardiac diseases, wherein the analytical model is apretrained Machine Learning (ML) model.

In yet another aspect, there are provided one or more non-transitorymachine-readable information storage mediums comprising one or moreinstructions, which when executed by one or more hardware processorscauses a method for screening and monitoring cardiac diseases byanalyzing acquired physiological signals, the method comprising:displaying a User Interface (UI), by one or more hardware processors ofa heart sense device, for enabling entering of metadata comprisingdemography and clinical information associated with a subject among aplurality of subjects screened and monitored via an authenticated accessto the heart sense device, wherein a plurality of probes of the heartsense device are non-invasively attached to the subject.

Further, synchronously acquiring by the one or more hardware processorsvia the plurality of probes, a plurality of physiological signalscomprising an ECG, a PPG, and a PCG of the subject, whereinsynchronously acquiring the plurality of physiological signalscomprises: acquiring each of the plurality of physiological signals as aplurality of segments of data; converting the plurality of segments ofdata corresponding to the physiological signals into a plurality ofdigital segments using an Analog to Digital Converter (ADC); associatingeach of the plurality of digital segments with time stamps;pre-processing each of the plurality of segments with time stamps todiscard noisy segments and identify a plurality of clean segments;identifying a set of synchronous segments from the clean segments basedon mapping time stamps, wherein each of the set of synchronous segmentscorresponds to each of the plurality of physiological signals, andwherein the set of synchronous segments are captured over a configurablepredetermined time interval; and displaying the set of synchronoussegments on the UI.

Furthermore, transmitting by the one or more hardware processors, theset of synchronous segments and the metadata of the subject to a cloudserver via an application on a mobile device, wherein the application onthe mobile device communicates with the heart sense device over a shortrange communication interface and enables editing of metadata andpreliminary analysis on the set of synchronous segments via anauthenticated access mechanism.

Furthermore, analyzing using an analytical model in the cloud server,the set of synchronous segments and the metadata of each of theplurality of subjects and predicting a cardiac disease among a pluralityof cardiac diseases, wherein the analytical model is a pretrainedMachine Learning (ML) model.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory onlyand are not restrictive of the invention, as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this disclosure, illustrate exemplary embodiments and, togetherwith the description, serve to explain the disclosed principles:

FIG. 1 illustrates an exemplary architecture of a system for screeningand monitoring of cardiac diseases by analyzing acquired physiologicalsignals, in accordance with some embodiments of the present disclosure.

FIGS. 2A and 2B illustrates components of a heart sensing device of thesystem of FIG. 1 for synchronous capture of physiological signals, inaccordance with some embodiments of the present disclosure.

FIG. 2C illustrates a real world synchronized waveforms of photoplethysmograph (PPG), phonocardiogram (PCG), and electrocardiogram (ECG)captured and displayed by the heart sensing device, in accordance withsome embodiments of the present disclosure.

FIGS. 3A and 3B depict a flow diagram illustrating a method forscreening and monitoring of cardiac diseases by analyzing acquiredphysiological signals using system depicted in FIG. 1, in accordancewith some embodiments of the present disclosure.

FIG. 4 illustrates data sampling and synchronization of thephysiological signals, in accordance with some embodiments of thepresent disclosure.

It should be appreciated by those skilled in the art that any blockdiagrams herein represent conceptual views of illustrative systems anddevices embodying the principles of the present subject matter.Similarly, it will be appreciated that any flow charts, flow diagrams,and the like represent various processes which may be substantiallyrepresented in computer readable medium and so executed by a computer orprocessor, whether or not such computer or processor is explicitlyshown.

DETAILED DESCRIPTION OF EMBODIMENTS

Exemplary embodiments are described with reference to the accompanyingdrawings. In the figures, the left-most digit(s) of a reference numberidentifies the figure in which the reference number first appears.Wherever convenient, the same reference numbers are used throughout thedrawings to refer to the same or like parts. While examples and featuresof disclosed principles are described herein, modifications,adaptations, and other implementations are possible without departingfrom the scope of the disclosed embodiments. It is intended that thefollowing detailed description be considered as exemplary only, with thetrue scope being indicated by the following claims.

The embodiments herein provide a system and method for screening andmonitoring of cardiac diseases by analyzing acquired physiologicalsignals. The system synchronously captures physiological signals such asphoto plethysmograph (PPG), phonocardiogram (PCG) and electrocardiogram(ECG) from subject(s) by synchronizing to a particular time stamp andbuilds an analytical model in the cloud for analyzing heart conditionsfrom the captured physiological signals. The system and method providesa fusion based approach of combining the captured physiological signalssuch as PPG, PCG and ECG along with other details such as subjectclinical information, demography information and so on. The analyticalmodel is pretrained using ECG. PPG and PCG along with metadataassociated with the subject such as demography and clinical information.

The heart condition or cardiac disease referred herein is CoronaryArtery Disease (CAD) and the analytical models built are for CAD.However, it can be understood that the system can be modified forscreening and monitoring subjects for any cardiac disease of interestthat is related to the ECG, PPG and PCG signals.

Major problem associated with CAD is that the manifestations of CADspecific markers are not always guaranteed in cardiovascular signals,alternatively referred herein as physiological signals. CAD is relatedto the partial/complete blockage of coronary arteries. The partialblockage in the peripheral arteries can co-exist across arteries. Asmentioned in literature, the basic pathological reason behind it isatherosclerosis which is a generalized process and can manifest in thePPG morphology CAD also affects the normal ECG morphology through S-Tdepression and inverted T wave. Stenosis in the artery causes vibrationduring blood flow. Although human ear cannot detect that very accuratelyusing a stethoscope, PCG signals, recorded using high quality digitalstethoscope shows high spectral power content in a region above 100 Hz.Table 1 provides a summary of the CAD related information that isexpected to be derived from the sensors and metadata.

TABLE 1 Data Features associated with CAD PCG CAD patients typicallyhave higher spectral components above 100 Hz, owing to valvular disease.Not always detected by human auditory system, can be detected by signalprocessing and ML Single lead ECG Morphological changes in ECG traces byCNN or other machine learning techniques. Although not always present,some discriminating patterns (e.g. Inverted T wave S-T segmentdepression) might be found PPG Numerous prior arts suggest HRV of CADpatient are found to be lesser. PPG can be used for unobtrusive low timeHRV monitoring Patient clinical and Cardiac risk estimation which maylead to demography information CAD (age, height, weight, BP, self andfamily medical history,)

However, due to many to one mapping between the disease and the sensorobservation, as given in Table 2, the classification of CAD using asingle marker often results in suboptimal performance. Hence therequired is to do an effective combination of features extracted frommultiple sensor signals using the means of ML for better accuracy.

TABLE 2 Abnormal heart Abnormal ECG, sound(S3 present abnormal STdepression/ or other high Disease HRV inverted T wave frequencycomponents CAD Yes Yes Yes AF Yes No Sometimes Diabetes Yes No No BenignNo No Yes cardiac murmur Stress Yes Sometimes No COPD Maybe No YesAsthma Mostly No Mostly No Yes, but different pattern

As can be in the above table 2, there are two problems with the abovesymptomatic analysis. Firstly, not all patients of CAD exhibit allmanifestations, there is a probability associated with the each of themanifestations like HRV, ECG anomalies and abnormal sound to be causeddue to the underlying condition of CAD. A true figure of prevalence foreach of these markers is unknown from literature and may be demographydependent. The second problem is that several of these manifestationswhen taken alone can be caused by a number of different underlyingconditions which may not even be of cardiac nature, like electrolyteimbalance and increased cranial pressure along with diabetes andhypertension etc.

However, referring to works in literature differential diagnosisapproach can be applied to conclude that all manifestations occurringsimultaneously may be caused by a single underlying condition, whichreduces the search space substantially by using multiple signals ofcardiac nature. Further, as mentioned in other prior work, if multiplefeatures are taken from the signals on a large cohort of control anddiseased population, a market basket analysis can be run on the space toscreen a particular disease like CAD with high probability. This is alsosupported by study on 150 subjects, as an effective fusion of multiplesensor signal boosts up the overall sensitivity to 0.9, which is higherthan any individual sensor data. Hence, there is a need for synchronizeddata capture of PPG, PCG and ECG on a large population of diseasedpatients and controls to prove the efficacy of the early screening ofCAD and possibly give indications on the severity of the diseasecondition.

Hence, the system disclosed herein analyses multiple such cardiovascularsignals by synchronous acquisition along with subject demography andclinical information, simultaneously for devising an early screeningsystem for CAD in an individual under screening. Thus, system fusesmultiple weak markers to predict more accurate CAD analysis andpredictions. Unlike, existing methods that focus only on ECG and PPG,the system disclosed herein includes PCG, which is one of the criticalfactor for accurate CAD analysis and predictions.

Referring now to the drawings, and more particularly to FIG. 1 throughFIG. 4, where similar reference characters denote corresponding featuresconsistently throughout the figures, there are shown preferredembodiments and these embodiments are described in the context of thefollowing exemplary system and/or method.

FIG. 1A illustrates an exemplary architecture of a system 100 for heartsensing monitoring of cardiac diseases by analyzing acquiredphysiological signals, in accordance with some embodiments of thepresent disclosure.

The architecture of system 100 comprises a heart sensing device 104 forsynchronous capture of physiological signals from a subject 102 underobservation. Further a mobile device 106 enables sharing of the acquiredhealth data corresponding to the physiological signals with a cloudserver 108 for analysis of the acquired health data of the subject 102using prior built analytical models. The heart sensing device 104enables synchronous data collection of physiological signals such asPPG, PCG, ECG from subject 102 using a set sensors attached to thesubject. The acquisition of synchronous physiological signals,interchangeably referred as signals, can be performed even by anon-expert such as a general health care professional, health attendantsand the like. The functions and components of the heart sensing device104 are further explained in conjunction with FIG. 2A and FIG. 2B. Thesynchronous physiological signals from heart sensing device 104 arefurther transferred to mobile device 106 using any short rangecommunication such as Bluetooth or the like. The mobile device 106 canserve or function as a digital platform to connect heart sensing device104 with the cloud server 108 comprising an analytical model (not shown)for cardiac data analysis. The mobile device 106 can be any digitaldevice such as tablet/phone, desktop and the like with an applicationinstalled, which enables communication with the heart sensing device104. The synchronous physiological signals are further uploaded to acloud server 108 using Wi-Fi/GSM. Further, any additionalinformation/metadata related to the acquired signals corresponding tothe subjects under screening can also be shared with the cloud server108. The uploaded synchronous physiological signals are analyzed for anycardiac health conditions with the help of the analytical model in thecloud server. The analysis results from the analytical model are furtheranalyzed/visualized by experts (doctors) for one or more heart diseasesbut not limited to such as Coronary Artery Disease (CAD), andhypertension.

The person under screening is the person who is attending the healthcheck-up camp. The person under screening is connected to the HeartSense Device through ECG cable, SpO2 sensor, and Digital Stethoscope.

A ECG cable is used to capture ECG signals through 3 lead or 5 leadcable. The ECG cable is connected to the person under screening throughthe adhesive/suction electrodes. A SpO2 sensor is connected to thepatient (subject 102) for getting PPG. diaphragm of stethoscope shall beplaced appropriately on chest of the subject 102 by the healthcareprofessional, wherein all the probe attachments or connections arenon-invasive.

The Healthcare professional is a qualified person to collect human vitalsigns. The healthcare professional is responsible for the accurateplacement of ECG electrodes, Digital Stethoscope and SpO2 probe. TheHealthcare Professional is only the operator to capture the vital signs.

FIGS. 2A, 2B and 2C illustrate components of the heart sensing device ofthe system of FIG. 1 for synchronous capture of physiological signals,in accordance with some embodiments of the present disclosure.

FIG. 2A is block diagram of the heart sensing device 104. In anembodiment, the system 100 includes a processor(s) 204, communicationinterface device(s), alternatively referred as input/output (I/O)interface(s) 206, and one or more data storage devices or a memory 202operatively coupled to the processor(s) 204. The heart sensing device104 with one or more hardware processors is configured to executefunctions of one or more functional blocks of the FIG. 2A. Referring tothe components of heart sensing device 104, in an embodiment, theprocessor(s) 204, can be one or more hardware processors 204. In anembodiment, the one or more hardware processors 204 can be implementedas one or more microprocessors, microcomputers, microcontrollers (asdepicted in FIG. 2B), digital signal processors, central processingunits, state machines, logic circuitries, and/or any devices thatmanipulate signals based on operational instructions. Among othercapabilities, the one or more hardware processors 204 are configured tofetch and execute computer-readable instructions stored in the memory202. In an embodiment, the heart sensing device can be implemented in avariety of computing systems including laptop computers, notebooks,hand-held devices such as mobile phones, workstations, mainframecomputers, servers, a network cloud and the like.

The I/O interface(s) 206 can include a variety of software and hardwareinterfaces, for example, a web interface, a graphical user interface, atouch user interface (TUI), interfaces for the ECG, PPG, PCG sensors andthe like and can facilitate multiple communications within a widevariety of networks N/W and protocol types, including wired networks,for example, LAN, cable, etc., and wireless networks, such as WLAN,cellular, or satellite. In an embodiment, the I/O interface (s) 206 caninclude one or more ports for connecting a number of devices (nodes) ofthe heart sensing device to one another or to another server such as thecloud server 108 via the mobile device 106.

The memory 202 may include any computer-readable medium known in the artincluding, for example, volatile memory, such as static random accessmemory (SRAM) and dynamic random access memory (DRAM), and/ornon-volatile memory, such as read only memory (ROM), erasableprogrammable ROM, flash memories, hard disks, optical disks, andmagnetic tapes.

Further, the memory 202 may include a database 208, which may storeinformation pertaining to input(s)/output(s) of each step performed bythe processor(s) 104 of the system 100 and methods of the presentdisclosure. For example, database may include the ECG, PPG and PCGsignal samples and metadata corresponding to the subjects beenmonitored. In an embodiment, the database 208 may be external (notshown) to the heart sensing device 100 and coupled to the system 100 viathe I/O interface 206. Functions of the components of heart sensingdevice 104 are explained in conjunction with FIG. 2B, 2C and flowdiagram of FIGS. 3A and 3B.

FIG. 2B illustrates components of the heart sensing device 104 used bythe system 100 of FIG. 1 for synchronous capture of physiologicalsignals from a subject for monitoring of cardiac diseases, in accordancewith some embodiments of the present disclosure. Three sensors ECG, PPGand PCG are connected to high speed Analog to Digital Convertor (ADC)channels. The signals from these sensors are captured in sequence withthe defined sampling rates. The signals are captured via Multiplexer(MUX) and fed to the ADC. ADC shares the converted analog to digitaldata to a microcontroller (processor(s) 204). The Microcontroller doesthe noise cancellation of the signal or discards incorrect data packetsbased on predefined packet structure. Microcontroller adds uniquecurrent Timestamp, packets identity headers and cyclic redundancy check(CRC) around each of the data packet. Timestamped packets helps machinelearning algorithms to correlate the time synchronized signals. Thesedata packets are finally forwarded to the mobile device 106 for furtherprocessing and sharing it with the cloud server 108.

The synchronous capture of the ECG, PPG and PCG is explained inconjunction with FIG. 4.

FIG. 2C illustrates a real world synchronized waveforms of ECG, PPG andPCG captured and displayed by the heart sensing device 104, inaccordance with some embodiments of the present disclosure. FIG. 2Cshows an aligned ECG/PCG and PPG waveforms on test user interface (UI)screen of the heart sensing device 104 after performing synchronizationof the captured physiological signals. The heart sensing device 104captures data with a drift of not more than 10 milliseconds. The heartsensing device 104 captures ECG signals at 300 samples per second, PPGat 100 samples per second and PCG at 8 kHz sampling rate. The heartsensing device 104 can store 1000 test data recordings of 3 minutes eachand with continuous usage the device can operate around 8 hours. For ECGin resting position, it is observed post study that minimum 2 minutes ofdata is required for machine learning for proper screening of diseases.Hence minimum 3 minutes predefined time interval is set for datacollection to improve the accuracy of predictions of the analyticalmodel. The heart sensing device 104 is battery operated portable device,with plug in for recharging for the battery and can work in non-aircondition dusty, hot and humid environments. The device comprises of atest screen user interface which displays parameters required to displaytest data and other vital parameters on screen. The test screen userinterface comprises 3 optimal parts. First/major part of the test screenuser interface area displays data received from PPG, PCG and ECG sensorsof the subject 102. Second part of the test screen user interface areadisplays vital parameters such as sensors connectivity status, samplingfrequency, pulse rate, respiration rate, peripheral capillary oxygensaturation (SPo2) and so on. Third part of the test screen userinterface provides buttons for user interaction to execute and save thetest data.

FIGS. 3A and 3B depicts a flow diagram illustrating a method 300 forscreening and monitoring of cardiac diseases by analyzing acquiredphysiological signals using system depicted in FIG. 1, in accordancewith some embodiments of the present disclosure. In an embodiment, theheart sensing device 104 comprises one or more data storage devices orthe memory 202 operatively coupled to the processor(s) 204 and isconfigured to store instructions for execution of steps of the method300 by the processor(s) or one or more hardware processors 204. Thesteps of the method 300 of the present disclosure will now be explainedwith reference to the components or blocks of the system 100 and theheart sensing device 104 as depicted in FIG. 1, FIG. 2A, FIG. 2B and thesteps of flow diagram as depicted in FIG. 3A and FIG. 3B along with timesequence diagram for synchronization of ECG, PPG and PCG as depicted inFIG. 4. Although process steps, method steps, techniques or the like maybe described in a sequential order, such processes, methods andtechniques may be configured to work in alternate orders. In otherwords, any sequence or order of steps that may be described does notnecessarily indicate a requirement that the steps to be performed inthat order. The steps of processes described herein may be performed inany order practical. Further, some steps may be performedsimultaneously.

Referring now to steps of method 300, at step 302 of the method 300 theone or more hardware processors (204) of the heart sensing device 104are configured to display a User Interface (UI) for enabling entering ofmetadata comprising demography and clinical information associated witha subject among a plurality of subjects screened and monitored via anauthenticated access to the heart sense device, wherein a plurality ofprobes of the heart sense device are non-invasively attached to thesubject.

At step 304 of the method 300 the one or more hardware processors (204)of the heart sensing device 104 are configured to synchronously acquirevia the plurality of probes, the plurality of physiological signals ofthe At step 304 of the method 300, the one or more hardware processors(204) are configured to subject 102, comprising an ECG, a PPG, and a PCGof the subject. The physiological signals are acquired under thesupervision of the health care professional, who is responsible for theaccurate placement of sensors. The heart sensing device 104 acquiresheart sound through e-Stethoscope. For listening the sound, heartsensing device 104 provides an option of connecting a headphone. Itprovides the healthcare professional to listen the sound as ine-Stethoscope. The acquired physiological signals per person will have 3minutes recording (configurable timings).

Synchronously acquiring the plurality of physiological signals is inaccordance to the explanation as provided in the hardware of the heartsensing device 104 as explained in conjunction with FIGS. 2B and 2C. Thesteps for synchronous acquisition of ECG, PPG and PCG comprise:

-   -   a) acquiring each of the plurality of physiological signals as a        plurality of segments of data (304 a).    -   b) converting the plurality of segments of data corresponding to        the physiological signals into a plurality of digital segments        using an Analog to Digital Converter (ADC) (304 b).    -   c) associating each of the plurality of digital segments,        interchangeably referred herein as packets with time stamps (304        c).    -   d) Pre-processing each of the time stamped plurality of segments        to discard noisy segments and identify clean segments (304 d).        The noise cancellation of the signal or discards incorrect data        packets (noisy segments) based on predefined packet structure.    -   e) Identifying a set of synchronous segments from the clean        segments based on mapping time stamps, wherein each of the set        of synchronous segments corresponds to each of the plurality of        physiological signals as explained in conjunction with FIG. 2B,        and wherein the set of synchronous segments are captured over a        configurable predetermined time interval (304 e). For example,        the interval may be 3 minutes window.    -   f) Displaying the set of synchronous segments on the UI (304 f)        as depicted in FIG. 2C.

At step 306 of the method 300, the one or more hardware processors (204)are configured to transmit the set of synchronous segments and themetadata of the subject to a cloud server via an application on themobile device 106. The application on the mobile device 106 communicateswith the heart sense device 104 over a short range communicationinterface. Further, the application enables editing of metadata andpreliminary analysis on the set of synchronous segments via anauthenticated access mechanism.

The synchronous physiological signals are collected on the mobile device106 which can be a digital device such as tablet/phone, desktop and soon connected via Bluetooth or wired connection to the heart sensingdevice 104. The collected synchronous physiological signals are uploadedto the cloud server 108 using Wi-Fi/GSM. The mobile device 106 comprisesa splash screen, login screen, subject list screen, add new subjectscreen, subject's metadata, subject data capture screen and adminscreen. The splash screen contains a logo screen for the heart sensingdevice 104. All users of the application can login by entering usernameand password details in the login screen of the mobile device 106. Thehealth care professional can view the details of the subject underconsideration in the subject list screen. The health care professionalcan add or modify information into the subject list using the add newsubject screen and also modify the subject metadata if needed. Using thedata capture screen, the health care professional can capture thephysiological signals PPG, PCG and ECG of the subject and automaticallyupload the captured signals to the cloud server 108 for further analysisfor heart health conditions. Admin screen is loaded after the loginscreen. Admin credentials are separate from normal user/healthcareprofessional. Admin screen provides an option to reset the board tofactory settings or modify the board configurations. Health careprofessionals are not provided with admin privileges.

At step 308 of the method 300, the one or more hardware processors (204)are configured to use the analytical model in the cloud server, the setof synchronous segments and the metadata of each of the plurality ofsubjects and predicting a cardiac disease such as CAD among a pluralityof cardiac diseases, The analytical model is a pretrained MachineLearning (ML) model. The method utilizes multiple techniques foranalyzing the synchronously captured signals for determining severalcardiac health conditions. Cardiac abnormality detection is performedbased on the physiological signals. Physiological signals like PPG, PCGand ECG are band-limited for normal subjects. The signatures ofabnormalities in the signals mostly appear in terms of spectralcomponents, beyond that region and mostly in the higher frequency zones(e.g. murmur, arrhythmias, and CAD). A one class classifier is designedto check whether the acquired physiological signals are normal. If theacquired physiological signals are not normal, they are further analyzedfor specific heart diseases.

The method proposed generates a plurality of features from the capturedphysiological signals PPG, PCG and ECG of the subject 102. A pluralityof relevant features are extracted from the generated plurality offeatures by applying a set of statistical measures such as mean,variance, and kurtosis. The relevant features extracted are classifiedusing algorithms to further analyze the various heart health conditionsof the subject 102. The analysis results are provided tospecialists/doctors for analyses/visualize. The specialist/doctor canview the results using a web console. Using the web console thespecialist/doctor can do the analyses/visualize the results for CADstatus, and hypertension and other cardiac issues.

For example a junior doctor determines a risk score by querying thesubject 102 under observation with relevant questions. If the risk scoredetermined is high, the authenticated health care professional capturesthe physiological signals of the subject with the help of the heartsensing device 104. The captured signals are uploaded to the analyticalmodel in the cloud server 108 are encrypted at the time of uploading forensuring security. The cloud server 108 has an authentication method forreceiving the uploaded signal data. A senior doctor analyzes the resultsof the analysis from the analytical model of the cloud server along withthe risk score for any heart health conditions. Further, the analysisresults are stored in memory for future analysis/visualization of theresults by the doctors or experts. Thus, system disclosed hereinenables:

-   -   a. Visualization of data for a physician in such a way that he        can quickly point out anomalies (marking S1. S2, R peaks etc)    -   b. Ability for doctor to mark notes for further algorithm        improvement

[Interpretable AI]

FIG. 4 illustrates data sampling and synchronization of thephysiological signals, in accordance with some embodiments of thepresent disclosure. The FIG. 4 depicts a sequence data diagram forsynchronous capture of ECG, PPG and PCG using an Network Time Protocol(NTP) available on the mobile device 106. Before the synchronous captureis initiated the mobile device 106 triggered by the application isconfigured to first update the device time with the Network TimeProtocol (NTP). Whenever data capture event is initiated, mobile device106 shares the NTP based Timestamp to microcontroller (processor 204) ofthe heart sensing device 104 to updates the clock time of themicrocontroller. After updating the time value, microcontroller triggersstart data command to sensors interface (I/O interface 206) to share thesensors data. Sensors interface shares the data in a predefined formatand sampling frequency. Microcontroller adds unique Current Timestamp,packets identity headers and cyclic redundancy check (CRC) around eachof the data packet. These data packets (segments) are finally forwardedto mobile device 106 for further processing and storage. The data iscollected around 3 minutes and data capture auto terminates after 3minutes or if explicit instruction of terminate data capture event areprovided by the end user (health professional) performed by the user.

Table 3 below depicts example sampling frequency values used for theECG, PPG and PCG signals.

TABLE 3 Average time Sampling required for Frequency Bytes received perSecond each sample ECG 300 (1 to 7 bytes length depending 3.3millisecond Samples/ upon channel (1 to 7) selection) Sec 1 * 300samples = 300 bytes/ Second . . . 7 * 300 samples = 2100 bytes/ SecondPPG 100 100 bytes/Second  10 millisecond Samples/ Sec PCG Audio AudioData in Wave Format — Recording Setting: 8000 Hz, Mono Channel, 16 bitPCM

As depicted from the table for ECG signal processing, usually 250 Hzsampling rate is highly acceptable, hence the heart sensing device 104is set to 300 Hz sampling rate for ECG. ECG signals supports single ormulti-channel data using heart sense device 104. Channels include I, II,III, aVR, aVL, aVF and C1. Each of the channel data is represented by asingle byte value. If only single channel is used for ECG data capture,then almost 300 data bytes are received per second and formulti-channel, data size is in multiples of channels selected.

For PPG signal, 60 Hz is standard sampling rate, hence the heart sensingdevice 104 is set to 100 Hz sampling rate for PPG. PPG data value isrepresented by a single byte value. Each second 100 bytes of data arereceived.

For PCG the heart sensing device 104 is set to 8 KHz sampling rate withaudio properties of 16-bit mono PCM data format. The sound datagenerated using this properties is good enough for machine learning.

Along with each of the data bytes unique timestamp, data byte identifierand cyclic redundancy check (CRC) packets are appended.

As mentioned earlier, for ECG in resting position, it is observed poststudy that minimum 2 minutes of data is required for machine learningfor proper screening of diseases. Hence minimum 3 minutes predefinedtime interval is set for data collection to improve the accuracy ofpredictions of the analytical model.

The written description describes the subject matter herein to enableany person skilled in the art to make and use the embodiments. The scopeof the subject matter embodiments is defined by the claims and mayinclude other modifications that occur to those skilled in the art. Suchother modifications are intended to be within the scope of the claims ifthey have similar elements that do not differ from the literal languageof the claims or if they include equivalent elements with insubstantialdifferences from the literal language of the claims.

It is to be understood that the scope of the protection is extended tosuch a program and in addition to a computer-readable means having amessage therein; such computer-readable storage means containprogram-code means for implementation of one or more steps of themethod, when the program runs on a server or mobile device or anysuitable programmable device. The hardware device can be any kind ofdevice which can be programmed including e.g. any kind of computer likea server or a personal computer, or the like, or any combinationthereof. The device may also include means which could be e.g. hardwaremeans like e.g. an application-specific integrated circuit (ASIC), afield-programmable gate array (FPGA), or a combination of hardware andsoftware means, e.g. an ASIC and an FPGA, or at least one microprocessorand at least one memory with software processing components locatedtherein. Thus, the means can include both hardware means, and softwaremeans. The method embodiments described herein could be implemented inhardware and software. The device may also include software means.Alternatively, the embodiments may be implemented on different hardwaredevices, e.g. using a plurality of CPUs.

The embodiments herein can comprise hardware and software elements. Theembodiments that are implemented in software include but are not limitedto, firmware, resident software, microcode, etc. The functions performedby various components described herein may be implemented in othercomponents or combinations of other components. For the purposes of thisdescription, a computer-usable or computer readable medium can be anyapparatus that can comprise, store, communicate, propagate, or transportthe program for use by or in connection with the instruction executionsystem, apparatus, or device.

The illustrated steps are set out to explain the exemplary embodimentsshown, and it should be anticipated that ongoing technologicaldevelopment will change the manner in which particular functions areperformed. These examples are presented herein for purposes ofillustration, and not limitation. Further, the boundaries of thefunctional building blocks have been arbitrarily defined herein for theconvenience of the description. Alternative boundaries can be defined solong as the specified functions and relationships thereof areappropriately performed. Alternatives (including equivalents,extensions, variations, deviations, etc., of those described herein)will be apparent to persons skilled in the relevant art(s) based on theteachings contained herein. Such alternatives fall within the scope ofthe disclosed embodiments. Also, the words “comprising,” “having,”“containing,” and “including,” and other similar forms are intended tobe equivalent in meaning and be open ended in that an item or itemsfollowing any one of these words is not meant to be an exhaustivelisting of such item or items, or meant to be limited to only the listeditem or items. It must also be noted that as used herein and in theappended claims, the singular forms “a,” “an,” and “the” include pluralreferences unless the context clearly dictates otherwise.

Furthermore, one or more computer-readable storage media may be utilizedin implementing embodiments consistent with the present disclosure. Acomputer-readable storage medium refers to any type of physical memoryon which information or data readable by a processor may be stored.Thus, a computer-readable storage medium may store instructions forexecution by one or more processors, including instructions for causingthe processor(s) to perform steps or stages consistent with theembodiments described herein. The term “computer-readable medium” shouldbe understood to include tangible items and exclude carrier waves andtransient signals, i.e., be non-transitory. Examples include randomaccess memory (RAM), read-only memory (ROM), volatile memory,nonvolatile memory, hard drives, CD ROMs, DVDs, flash drives, disks, andany other known physical storage media.

It is intended that the disclosure and examples be considered asexemplary only, with a true scope of disclosed embodiments beingindicated by the following claims.

What is claimed is:
 1. A processor implemented method for screening andmonitoring cardiac diseases by analyzing acquired physiological signals,the method comprising: displaying a User Interface (UI), by one or morehardware processors of a heart sense device, for enabling entering ofmetadata comprising demography and clinical information associated witha subject among a plurality of subjects screened and monitored via anauthenticated access to the heart sense device, wherein a plurality ofprobes of the heart sense device are non-invasively attached to thesubject; synchronously acquiring, by the one or more hardware processorsvia the plurality of probes, a plurality of physiological signalscomprising an ECG, a PPG, and a PCG of the subject, whereinsynchronously acquiring the plurality of physiological signalscomprises: a) acquiring each of the plurality of physiological signalsas a plurality of segments of data; b) converting the plurality ofsegments of data corresponding to the physiological signals into aplurality of digital segments using an Analog to Digital Converter(ADC); c) associating each of the plurality of digital segments withtime stamps; d) pre-processing each of the plurality of digital segmentswith the time stamps to discard noisy segments and identify a pluralityof clean segments; e) identifying a set of synchronous segments from theplurality of clean segments, based on mapping time stamps, wherein eachof the set of synchronous segments corresponds to each of the pluralityof physiological signals, and wherein the set of synchronous segmentsare captured over a configurable predetermined time interval; and f)displaying the set of synchronous segments on the UI; and transmitting,by the one or more hardware processors, the set of synchronous segmentsand the metadata of the subject to a cloud server via an application ona mobile device, wherein the application on the mobile devicecommunicates with the heart sense device over a short rangecommunication interface and enables editing of metadata and preliminaryanalysis on the set of synchronous segments via an authenticated accessmechanism.
 2. The method of claim 1, wherein the method comprisesanalyzing using an analytical model in the cloud server, the set ofsynchronous segments and the metadata of each of the plurality ofsubjects and predicting a cardiac disease among a plurality of cardiacdiseases, wherein the analytical model is a pretrained Machine Learning(ML) model.
 3. The method of claim 2, wherein the method comprisesdisplaying the predicted cardiac disease on the mobile device.
 4. Themethod of claim 1, wherein the heart sense device is a portable batteryoperated device.
 5. A system for screening and monitoring cardiacdiseases by analyzing acquired physiological signals, the systemcomprising: a heart sensing device, a mobile device, and a cloud server,wherein: the heart sensing device comprises a memory storinginstructions; one or more Input/Output (I/O) interfaces; and one or morehardware processors coupled to the memory via the one or more I/Ointerfaces, wherein the one or more hardware processors are configuredby the instructions to: display a User Interface (UI) for enablingentering of metadata comprising demography and clinical informationassociated with a subject among a plurality of subjects screened andmonitored via an authenticated access to the heart sense device, whereina plurality of probes of the heart sense device are non-invasivelyattached to the subject; synchronously acquire via the plurality ofprobes, a plurality of physiological signals comprising an ECG, a PPG,and a PCG of the subject, wherein synchronously acquiring the pluralityof physiological signals comprises: a) acquiring each of the pluralityof physiological signals as a plurality of segments of data; b)converting the plurality of segments of data corresponding to thephysiological signals into a plurality of digital segments using anAnalog to Digital Converter (ADC); c) associating each of the pluralityof digital segments with time stamps; d) pre-processing each of theplurality of digital segments associated with the time stamps to discardnoisy segments and identify a plurality of clean segments; e)identifying a set of synchronous segments from the plurality of cleansegments based on mapping time stamps, wherein each of the set ofsynchronous segments corresponds to each of the plurality ofphysiological signals, and wherein the set of synchronous segments arecaptured over a configurable predetermined time interval; and f)displaying the set of synchronous segments on the UI; and transmit theset of synchronous segments and the metadata of the subject to a cloudserver via an application on a mobile device, wherein the application onthe mobile device communicates with the heart sense device over a shortrange communication interface and enables editing of metadata andpreliminary analysis on the set of synchronous segments via anauthenticated access mechanism.
 6. The system of claim 5, wherein thecloud server is configured to analyze, using an analytical model, theset of synchronous segments and the metadata of each of the plurality ofsubjects and predicting a cardiac disease among a plurality of cardiacdiseases, wherein the analytical model is a pretrained Machine Learning(ML) model.
 7. The system of claim 6, wherein the cloud server isconfigured to communicate the predicted cardiac disease to the mobiledevice, and wherein the mobile device is configured to display thepredicted cardiac disease on the mobile device.
 8. The system of claim5, wherein the heart sensing device is a portable battery operateddevice.
 9. One or more non-transitory machine-readable informationstorage mediums comprising one or more instructions, which when executedby one or more hardware processors causes a method for screening andmonitoring cardiac diseases by analyzing acquired physiological signals,the method comprising: displaying a User Interface (UI) for enablingentering of metadata comprising demography and clinical informationassociated with a subject among a plurality of subjects screened andmonitored via an authenticated access to the heart sense device, whereina plurality of probes of the heart sense device are non-invasivelyattached to the subject; synchronously acquiring via the plurality ofprobes, a plurality of physiological signals comprising an ECG, a PPG,and a PCG of the subject, wherein synchronously acquiring the pluralityof physiological signals comprises: a) acquiring each of the pluralityof physiological signals as a plurality of segments of data; b)converting the plurality of segments of data corresponding to thephysiological signals into a plurality of digital segments using anAnalog to Digital Converter (ADC); c) associating each of the pluralityof digital segments with time stamps; d) pre-processing each of theplurality of digital segments with the time stamps to discard noisysegments and identify a plurality of clean segments; e) identifying aset of synchronous segments from the plurality of clean segments, basedon mapping time stamps, wherein each of the set of synchronous segmentscorresponds to each of the plurality of physiological signals, andwherein the set of synchronous segments are captured over a configurablepredetermined time interval; and f) displaying the set of synchronoussegments on the UI; and transmitting the set of synchronous segments andthe metadata of the subject to a cloud server via an application on amobile device, wherein the application on the mobile device communicateswith the heart sense device over a short range communication interfaceand enables editing of metadata and preliminary analysis on the set ofsynchronous segments via an authenticated access mechanism.
 10. The oneor more non-transitory mediums of claim 9, further comprises analyzingusing an analytical model in the cloud server, the set of synchronoussegments and the metadata of each of the plurality of subjects andpredicting a cardiac disease among a plurality of cardiac diseases,wherein the analytical model is a pretrained Machine Learning (ML)model.